scholarly journals Composite indicators – methodology and practical aspects

2009 ◽  
Vol 50 ◽  
Author(s):  
Jurga Rukšėnaitė
Keyword(s):  
Composite Indicators ◽  
Arima Models ◽  
Ranking Procedures

In this paper methodology of construction Composite Indicators is described. Data standardisation and ranking procedures are proposed. Results of modelling irregular component using different ARIMA models are described.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

scholarly journals Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 119
Author(s):  
Pitshu Mulomba Mukadi ◽  
Concepción González-García
Keyword(s):  
Time Series ◽  
Temperature Series ◽  
Climatic Variables ◽  
Monthly Precipitation ◽  
Agricultural Crops ◽  
Arima Models ◽  
Cover Time ◽  
Pests And Diseases ◽  
Historical Series ◽  
Preliminary Phase

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops.

Missing observations in ARIMA models: Skipping approach versus additive outlier approach

1999 ◽  
Vol 88 (2) ◽  
pp. 341-363 ◽  
Author(s):  
Vı́ctor Gómez ◽  
Agustı́n Maravall ◽  
Daniel Peña
Keyword(s):  
Missing Observations ◽  
Arima Models ◽  
Additive Outlier

Development of Composite Indicators of Cyclical Response in Business Surveys

2021 ◽  
Vol 28 (2) ◽  
pp. 24-41
Author(s):  
L. A. Kitrar ◽  
T. M. Lipkind
Keyword(s):  
Large Scale ◽  
Integrated Approach ◽  
Early Response ◽  
Business Environment ◽  
Economic Behavior ◽  
Retail Trade ◽  
Composite Indicators ◽  
Business Activity ◽  
Short Term ◽  
Current Output

The article proposes a new set of composite indicators-predictors in business tendency surveys, which allow identifying early information signals of a cyclical nature in the economic behavior of business agents. The main criterion for the efficiency of such indicators is their sensitivity to a cyclical pattern and changes in the dynamics of statistical referents. Property such as a statistically significant lead in time series or earlier publication allows them to be combined into indicators of early response. The composite Business Activity Indicator (BAI) in the basic sectors of the Russian economy is calculated by the authors for the first time based on the results of regular (monthly and quarterly) business surveys of Rosstat for 1998–2020 with a large-scale coverage of sampling units. In 2020, the number of survey respondents averaged about 20,000 organizations of all sizes. The index reflects the «common» profile in the dynamics of short-term fluctuations of the key parameters of the economic environment, which consists of the «balances of opinions» of respondents to the questions unified for all sectoral surveys and connected with the reference quantitative statistics with cross-correlation coefficients that are statistically significantly different from zero, with a lead at least one quarter. This is its main difference from the well-known indices of economic sentiment and entrepreneurial confidence. The main components of the BAI are the new composite indices of real demand, current output, real employment, total profits and economic situation. They aggregate the relevant «order» statistics for the basic sectors of the national economy, including the main kinds of industrial activities, retail trade, construction, and services.The article provides a methodological substantiation and an extended procedure for identifying the BAI components; their composition is formed for the entire set of retrospective results of business tendency monitoring in Russia. A new Aggregate Economic Vulnerability Indicator with a counterdirectional profile and varying degrees of symmetry of its dynamics relative to the short-term movement of the BAI is being introduced as the main limitation of business activity. Proactive monitoring of emerging vulnerabilities in the business environment is necessary to warn their large-scale accumulation, prevent the risks of economic downturns and ensure the highest possible macroeconomic stability. This integrated approach makes it possible to determine the novelty of the proposed measurements of short-term cyclical fluctuations in economic development.

Measuring Government Capacity and Constructing Composite Indicators: Three Essentials of Capacity

2018 ◽  
Vol 29 (3) ◽  
pp. 143-158
Author(s):  
Kun Yoon ◽  
Woohyun Shim ◽  
Jungwon Park ◽  
Younhee Kim
Keyword(s):  
Composite Indicators ◽  
Government Capacity
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